Time reverse reservoir localization

CROSS-REFERENCE TO RELATED APPLICATION

0001This application is a continuation application of U.S. patent application Ser. No. 12/017,527, Filed on Jan. 22, 2008 now U.S. Pat. No. 7,675,815 issued Mar. 9, 2010, hereby incorporated by reference in its entirety, which claims the benefit of U.S. Provisional Application No. 60/885,887 filed 20 Jan. 2007, U.S. Provisional Application No. 60/891,286 filed 23 Feb. 2007 and U.S. Provisional Application No. 60/911,283 filed 12 Apr. 2007.

BACKGROUND OF THE DISCLOSURE

00021. Technical Field

0003The disclosure is related to seismic exploration for oil and gas, and more particularly to determination of the positions of subsurface reservoirs.

00042. Description

0005Expensive geophysical and geological exploration investment for hydrocarbons is often focused on acquiring data in the most promising areas using relatively slow methods, such as reflection seismic data acquisition and processing. The acquired data are used for mapping potential hydrocarbon-bearing areas within a survey area to optimize exploratory or production well locations and to minimize costly non-productive wells.

0006The time from mineral discovery to production may be shortened if the total time required to evaluate and explore a survey area can be reduced by applying geophysical methods alone or in combination. Some methods may be used as a standalone decision tool for oil and gas development decisions when no other data is available.

0007Geophysical and geological methods are used to maximize production after reservoir discovery as well. Reservoirs are analyzed using time lapse surveys (i.e. repeat applications of geophysical methods over time) to understand reservoir changes during production. The process of exploring for and exploiting subsurface hydrocarbon reservoirs is often costly and inefficient because operators have imperfect information from geophysical and geological characteristics about reservoir locations. Furthermore, a reservoir's characteristics may change as it is produced.

0008The impact of oil exploration methods on the environment may be reduced by using low-impact methods and/or by narrowing the scope of methods requiring an active source, including reflection seismic and electromagnetic surveying methods. Various geophysical data acquisition methods have a relatively low impact on field survey areas. Low-impact methods include gravity and magnetic surveys that may be used to enrich or corroborate structural images and/or integrate with other geophysical data, such as reflection seismic data, to delineate hydrocarbon-bearing zones within promising formations and clarify ambiguities in lower quality data, e.g. where geological or near-surface conditions reduce the effectiveness of reflection seismic methods.

SUMMARY

0009A method and system for processing synchronous array seismic data includes acquiring synchronous passive seismic data from a plurality of sensors to obtain synchronized array measurements. A reverse-time data process is applied to the synchronized array measurements to obtain a plurality of dynamic particle parameters associated with subsurface locations. These dynamic particle parameters are stored in a form for display. Maximum values of the dynamic particle parameters may be interpreted as reservoir locations. The dynamic particle parameters may be particle displacement values, particle velocity values, particle acceleration values or particle pressure values. The sensors may be three-component sensors. Zero-phase frequency filtering of different ranges of interest may be applied. The data may be resampled to facilitate efficient data processing.

BRIEF DESCRIPTION OF THE DRAWINGS

0010<figref idref="DRAWINGS">FIG. 1</figref> is a schematic illustration of a method according to an embodiment of the present disclosure for calculating maximum values for subsurface locations from continuous synchronous signals;

0011<figref idref="DRAWINGS">FIG. 2</figref> illustrates various non-limiting possibilities for arrays of sensor for data acquisition of synchronous signals;

0012<figref idref="DRAWINGS">FIG. 3</figref> is a flow chart of reverse-time processing for application to seismic data;

0013<figref idref="DRAWINGS">FIG. 4</figref> is a flow chart of a data processing flow that includes acquiring or determining a velocity model associated with reverse-time processing of field data;

0014<figref idref="DRAWINGS">FIG. 5</figref> illustrates a model setup for determining a synthetic velocity model;

0015<figref idref="DRAWINGS">FIG. 6</figref> illustrates snapshots in time of reverse-time and forward-time processing for comparison;

0016<figref idref="DRAWINGS">FIG. 7</figref> illustrates maximum values of particle dynamic values plotted for determination of reservoir positions;

0017<figref idref="DRAWINGS">FIG. 8A</figref> illustrates a real input velocity model for reverse-time processing.

0018<figref idref="DRAWINGS">FIG. 8B</figref> illustrates a synthetic data example with the real velocity model of <figref idref="DRAWINGS">FIG. 8A</figref> showing that a reverse simulation of synthetic signals with source at the location of the assumed reservoir shows that the location of the source can be identified very well in this complex media.

0019<figref idref="DRAWINGS">FIG. 8C</figref> illustrates reverse time migration with real field data output as maximum dynamic particle parameter (in this illustration, velocity) results with a reservoir location; and

0020<figref idref="DRAWINGS">FIG. 9</figref> is diagrammatic representation of a machine in the form of a computer system within which a set of instructions, when executed may cause the machine to perform any one or more of the methods and processes described herein.

DETAILED DESCRIPTION

0021Information to determine the location of hydrocarbon reservoirs may be extracted from naturally occurring seismic waves and vibrations measured at the earth's surface using passive seismic data acquisition methods. Seismic wave energy emanating from subsurface reservoirs, or otherwise altered by subsurface reservoirs, is detected by arrays of sensors and the energy back-propagated with reverse-time processing methods to locate the source of the energy disturbance. An inversion methodology for locating positions of subsurface reservoirs may be based on various time reversal processing algorithms of time series measurements of passive seismic data.

0022Passive seismic data acquisition methods rely on seismic energy from sources not directly associated with the data acquisition. In passive seismic monitoring there may be no actively controlled and triggered source. Examples of sources recorded that may be recorded with passive seismic acquisition are microseisms (e.g., rhythmically and persistently recurring low-energy earth tremors), microtremors and other ambient or localized seismic energy sources.

0023Microtremors are attributed to the background energy normally present in the earth. Microtremor seismic waves may include sustained seismic signals within various or limited frequency ranges. Microtremor signals, like all seismic waves, contain information affecting spectral signature characteristics due to the media or environment that the seismic waves traverse as well as the source of the seismic energy. These naturally occurring and often relatively low frequency background seismic waves (sometimes termed noise or hum) of the earth may be generated from a variety of sources, some of which may be unknown or indeterminate.

0024Characteristics of microtremor seismic waves in the “infrasonic” range may contain relevant information for direct detection of subsurface properties including the detection of fluid reservoirs. The term infrasonic may refer to sound waves below the frequencies of sound audible to humans, and nominally includes frequencies under 20 Hz.

0025Synchronous arrays of sensors are used to measure vertical and horizontal components of motion due to background seismic waves at multiple locations within a survey area. The sensors measure orthogonal components of motion simultaneously.

0026Local acquisition conditions within a geophysical survey may affect acquired data results. Acquisition conditions impacting acquired signals may change over time and may be diurnal. Other acquisition conditions are related to the near sensor environment. These conditions may be accounted for during data reduction.

0027The sensor equipment for measuring seismic waves may be any type of seismometer for measuring particle dynamics, such as particle displacements or derivatives of displacements. Seismometer equipment having a large dynamic range and enhanced sensitivity compared with other transducers, particularly in low frequency ranges, may provide optimum results (e.g., multicomponent earthquake seismometers or equipment with similar capabilities). A number of commercially available sensors utilizing different technologies may be used, e.g. a balanced force feed-back instrument or an electrochemical sensor. An instrument with high sensitivity at very low frequencies and good coupling with the earth enhances the efficacy of the method.

0028Noise conditions representative of seismic waves that may have not traversed or been affected by subsurface reservoirs can negatively affect the recorded data. Techniques for removing unwanted noise and artifacts and artificial signals from the data, such as cultural and industrial noise, are important where ambient noise is relatively high compared with desired signal energy.

0029Time-reverse data processing may be used to localize relatively weak seismic events or energy, for example if a reservoir acts as an energy source or significantly affects acoustic energy traversing the reservoir. The seismograms measured at a synchronous array of sensor stations are reversed in time and used as boundary values for the reverse processing. Time-reverse data processing is able to track down event or energy sources for an S/N-ratio lower than one.

0030Field surveys have shown that hydrocarbon reservoirs may act as a source of low frequency seismic waves and these signals are sometimes termed “hydrocarbon microtremors.” The frequency ranges of microtremors have been reported between ˜1 Hz to 6 Hz or greater. A direct and efficient detection of hydrocarbon reservoirs is of central interest for the development of new oil or gas fields. One approach is to apply a time-reverse processing/migration. If there is a steady source origin (or other alteration) of low-frequency seismic waves within a reservoir, the location of the reservoir may be located using time reverse migration and may also be used to locate and differentiate stacked reservoirs.

0031Time reverse processing (or migration) of acquired seismic data, which may be in conjunction with modeling, using a grid of nodes is an effective tool to detect the locality of a steady origin of low-frequency seismic waves. As a non-limiting example for the purposes of illustration, microtremors may comprise low-frequency signals with a fundamental frequency of about 3 Hz and a range between 1.5 Hz and 4.5 Hz. Hydrocarbon affected seismic data that include microtremors may have differing values that are reservoir or case specific. Snapshots (images of an inversion representing one or more time steps) showing a current dynamic particle motion value (e.g., displacement, velocity, acceleration or pressure) at every grid point may be produced at specific time steps during the reverse-time signal processing. Data for nodes representing high or maximum particle velocity values indicate the location of a specific source (or a location related to seismic energy source aberration) of the forward or field acquired data. The maximum velocities obtained from the reverse-time data processing may be used to delineate parameters associated with the subsurface reservoir location.

0032There are many known methods for a reverse-time data process for seismic wave field imaging with Earth parameters from inversions of acquired seismic data. For example, finite-difference, ray-tracing and pseudo-spectral computations, in two- and three-dimensional space, are used for full or partial wave field simulations and imaging of seismic data. Reverse-time migration algorithms may be based on finite-difference, ray-tracing or pseudo-spectral wave field extrapolators. Output from these reverse-time data processing routines may include amplitudes for displacement, velocity, acceleration or pressures values at every time steps of the inversion.

0033<figref idref="DRAWINGS">FIG. 1</figref> illustrates a method according to a non-limiting embodiment of the present disclosure that includes using passively acquired seismic data to determine a subsurface location for hydrocarbons or other reservoir fluids. The embodiment, which may include one or more of the following (in any order), includes acquiring synchronous array seismic data having a plurality of components <b>101</b>. The acquired data from each sensor station may be time stamped and include multiple data vectors. An example is passive seismic data, such as multicomponent seismometry data from “earthquake” type sensors. The multiple data vectors may each be associated with an orthogonal direction of movement. Data may be acquired as orthogonal component vectors. The vector data may be arbitrarily mapped or assigned to any coordinate reference system, for example designated east, north and depth (e.g., respectively, Ve, Vn and Vz) or designated V_x, V_yand V_zaccording to any desired convention and is amenable to any coordinate system.

0034Data may be acquired with arrays, which may be 2D or 3D, or even arbitrarily positioned sensors <b>201</b> as illustrated in <figref idref="DRAWINGS">FIG. 2</figref>. <figref idref="DRAWINGS">FIG. 2</figref> illustrates various acquisition geometries which may be selected based on operational considerations. Array <b>220</b> is a 2D array and while illustrated with regularly spaced sensors <b>201</b>, regular distribution is not a requirement. Array <b>230</b> and <b>240</b> are example illustrations of 3D arrays. Sensor distribution <b>250</b> could be considered an array of arbitrarily placed sensors and may even provide for some modification of possible spatial aliasing that can occur with regular spaced sensor <b>201</b> acquisition arrays.

0035While data may be acquired with multi-component earthquake seismometer equipment with large dynamic range and enhanced sensitivity, many different types of sensor instruments can be used with different underlying technologies and varying sensitivities. Sensor positioning during recording may vary, e.g. sensors may be positioned on the ground, below the surface or in a borehole. The sensor may be positioned on a tripod or rock-pad. Sensors may be enclosed in a protective housing for ocean bottom placement. Wherever sensors are positioned, good coupling results in better data. Recording time may vary, e.g. from minutes to hours or days. In general terms, longer-term measurements may be helpful in areas where there is high ambient noise and provide extended periods of data with fewer noise problems.

0036The layout of a data survey may be varied, e.g. measurement locations may be close together or spaced widely apart and different locations may be occupied for acquiring measurements consecutively or simultaneously. Simultaneous recording of a plurality of locations (a sensor array) may provide for relative consistency in environmental conditions that may be helpful in ameliorating problematic or localized ambient noise not related to subsurface characteristics of interest. Additionally the array may provide signal differentiation advantages due to commonalities and differences in the recorded signal.

0037Returning to <figref idref="DRAWINGS">FIG. 1</figref>, the data may be optionally conditioned or cleaned as necessary <b>103</b> to account for unwanted noise or signal interference. For example various processing steps may be performed such as offset removal, detrending the signal and band pass or other targeted frequency filtering. The vector data may be divided into selected time windows <b>105</b> for processing. The length of time windows for analysis may be chosen to accommodate processing or operational concerns.

0038If a hydrocarbon signature is known or expected for a range of frequencies an optional frequency filter (e.g., zero phase, Fourier or other wavelet type) may be applied <b>107</b> to condition the data for processing. Examples of basis functions for filtering or other processing operations include without limitation the classic Fourier transform or one of the many Continuous Wavelet Transforms (CWT) or Discreet Wavelet Transforms. Examples of other transforms include Haar transforms, Haademard transforms and Wavelet Transforms. The Morlet wavelet is an example of a wavelet transform that often may be beneficially applied to seismic data. Wavelet transforms have the attractive property that the corresponding expansion may be differentiable term by term when the seismic trace is smooth.

0039Additionally, signal analysis, filtering, and suppressing unwanted signal artifacts may be carried out efficiently using transforms applied to the acquired data signals. Additionally the data may be resampled <b>108</b> to facilitate more efficient processing.

0040The earth velocity model or velocity structure, which may be developed from predetermined subsurface velocity information, for use with the reverse-time processing may be input to the work flow at virtually any point, but is illustrated <b>110</b> as an example. The velocity model may be resampled to facilitate data processing as well.

0041Inverting field-acquired passive seismic data to determine the location of subsurface reservoirs includes using the acquired time-series data as ‘sources’ in reverse-time processing <b>109</b>. The output of the reverse-time processing includes a measure of the dynamic particle motion of sources associated with subsurface positions (which may be nodes of mathematical descriptions (i.e., models) of the earth). The maximum values derived from dynamic particle motion, which may be displacements, velocities or accelerations, may be collected <b>111</b> to determine the energy source location contributing to the dynamics. Plotting the maximum dynamic values <b>113</b> from all the measurement values output from a reverse-time process may provide a basis for interpreting the location of a subsurface reservoir. The amplitude values associated with subsurface locations having the highest relative values may indicate the position of a reservoir that is the source of hydrocarbon tremors (for example <figref idref="DRAWINGS">FIG. 7</figref>). An alternative to checking and storing an updated maximum for every backward time step is to sum together all the values calculated for each time step or subsurface position. The data, whether maximum values or summed values, may be contoured or otherwise graphically displayed to illuminate reservoir positions (for example <b>90</b> in <figref idref="DRAWINGS">FIGS. 8</figref><i>b </i>and <b>92</b> and <b>94</b> in <figref idref="DRAWINGS">FIG. 8</figref><i>c</i>).

0042A non-limiting example of a reverse-time processing inversion is illustrated in <figref idref="DRAWINGS">FIG. 3</figref> wherein data are input <b>301</b> to the processing flow. The data may optionally be filtered to a selected frequency range. A velocity model for the reverse-time process may be determined from known information <b>303</b> or estimated. A wave-equation reverse-time inversion is performed <b>305</b> to obtain particle dynamic behavior <b>307</b>.

0043The reverse-time inversion process may include development of an earth model that may be based on a priori knowledge or estimates of a survey area of interest. During data preparation, the forward modeling inversion may be useful for anticipating and accounting for known seismic signal or refining the velocity field used for the reverse time processing. Modeling may include accounting for, or the removal of, the near sensor signal contributions due to environmental field effects, unwanted signal and noise and, thus, the isolation of those parts of signals believed to be associated with environmental components being examined. By adapting or filtering the data between successive iterations in the inversion process, predicted signal can be obtained, thus allowing convergence to a structure element indicating whether a reservoir is present within the subsurface.

0044One embodiment for determining reservoir location includes acquiring synchronous passive seismic data as continuous (digital or analog) signals acquired with arrays of seismometers. Seismic data parameters are determined from the acquired data.

0045<figref idref="DRAWINGS">FIG. 4</figref> illustrates an example of a reverse-time process inversion for locating a reservoir in the subsurface using a velocity model <b>402</b> as input for a reverse-time migration of continuous signals. The reverse time migration may be wave equation based. Any available geosciences information <b>401</b> may be used as input to determine parameters for an initial model <b>402</b> that may be modified as input to a reverse-time data process for continuous signals <b>403</b> as more information is available or determined. Synchronously acquired passive seismic data <b>405</b> are input (after any optional processing/conditioning) to the reverse-time data process <b>403</b>. Particle dynamics such as displacement, velocity or acceleration (or pressure) are determined from the processed data for determining dynamic particle behaviour <b>404</b>. Maximum values may be determined <b>406</b> and stored <b>410</b> to determine subsurface reservoir positions.

0046The maximum amplitude values associated with the dynamic particle behavior, such as velocity values, represent the location of sources of hydrocarbon tremors. Unlike prior art time-reverse methods, there is no specific time associated with the source, since the tremor as the source is a continuous function unlike discrete seismic events. Not only the tremor source may be located, but noise sources not related to tremor sources may be differentiated as well.

0047An example of an embodiment illustrated here uses a numerical modeling algorithm similar to the rotated staggered grid finite-difference technique described by Saenger et al. (2000). The two dimensional numerical grid is rectangular. Computations may be performed with second order spatial explicit finite difference operators and with a second order time update. However, as will be well known by practitioners familiar with the art, many different reverse-time methods may be used along with various wave equation approaches. Extending methods to three dimensions is straightforward.

0048For one non-limiting illustrative example used herein, a model data set rather than acquired data are input. The grid of the mathematical model contains 901 horizontal and 301 vertical nodal points with an interval of 10 m in both directions. The model setup is similar to the geological situation illustrated in <figref idref="DRAWINGS">FIG. 5</figref>. For simplicity each model unit is homogeneous and isotropic, though there is no limit on the potential complexity of the situation. There are ten different non-planar sediment model units with P-wave velocities increasing from 1200 m/s (top layer) stepwise by 200 m/s up to 3000 m/s (bottom layer). The velocity is defined by varying Young's Modulus and a constant density of 2000 kg/m³is applied for all sediment units. The crystalline basement model unit is defined by a density of 3000 kg/m³and a Young's Modulus of 1.08*10¹¹N/m²resulting in a P-wave velocity of 6000 m/s. The lower part of the model is cut by a zone <b>501</b> with a density of 2000 kg/m³and a Young's Modulus of 8*10⁹N/m²resulting in a P-wave velocity of 2000 m/s. The reservoirs (Reservoir <b>1</b> and Reservoir <b>2</b>) with a thickness of about 50 m and a lateral extension of about 2000 m are positioned close to the middle of the model domain. The reservoirs have a density of 2000 kg/m³and a Young's Modulus of 1.25*10¹⁰N/m²resulting in a P-wave velocity of 2500 m/s. All S-wave velocities are a multiple of approximately 1.4 smaller than the corresponding P-wave velocity.

0049<figref idref="DRAWINGS">FIG. 5</figref> consists of ten sediment units and a basement unit. The lower parts of both models are separated from the tops by a zone <b>501</b> with a P-wave velocity of 2000 m/s. For the top model one reservoir, Reservoir <b>1</b>, defines seismic source area. The second model includes two source areas (Reservoir <b>1</b> and Reservoir <b>2</b>) as represented by two stacked reservoirs.

0050A time-reverse inversion may be conducted for the each of the <figref idref="DRAWINGS">FIG. 5</figref> cases, one for the single reservoir case (top model) and another time-reverse inversion may be conducted for the two stacked reservoirs case (bottom model). Snapshots as illustrated in <figref idref="DRAWINGS">FIG. 6</figref> during the reverse processing show an accumulation of high velocities in the vicinity of the sources which were applied in the forward model. Any apparent inaccuracy near the surface is, as discussed by Gajewski et al. (2005), considerably small because the error of maximal 100 m is much smaller than the wavelength of the waves at the central frequency. <figref idref="DRAWINGS">FIG. 6</figref> illustrates the forward model (left column corresponding to field data) and time reverse inversion (right column) for the single reservoir model. The top figures show the first steps of both simulations. The bottom figures correspond to the same time. The microtremors with a known source from the forward data (such as field acquired data) are visible in the time reverse (lower right) model.

0051The area where the highest velocities occur during the reverse modeling delineates the area in which the point (reservoir) sources of the forward model were distributed is illustrated in <figref idref="DRAWINGS">FIG. 7</figref>. The accumulation of high velocities is dense enough to distinguish between the two stacked reservoirs. Microtremor reservoir sources in the subsurface can be localized with time reverse methods. Both models show a focus of high velocities in the area of the reservoirs, which are zones of microtremor sources in the forward models.

0052<figref idref="DRAWINGS">FIG. 8A</figref> is illustrative of a velocity structure model for a field area that in general consists of a low velocity top layer <b>82</b>, a thick intermediate velocity layer <b>84</b> with low velocity contrast <b>86</b> relative to the top layer and a crystalline basement <b>88</b> of high velocity. <figref idref="DRAWINGS">FIG. 8B</figref> is illustrative of a reverse simulation of synthetic signals with source at the location <b>90</b> of the assumed reservoir shows that the location of the source can be identified very well in this complex media. <figref idref="DRAWINGS">FIG. 8C</figref> is illustrative of time reverse modeling with actual field data from the field area, which are ‘passive’ field measurement data that show a pattern similar to the synthetic example with reservoir locations <b>92</b> and <b>94</b>.

0053In one non-limiting embodiment a method and system for processing synchronous array seismic data includes acquiring synchronous passive seismic data from a plurality of sensors to obtain synchronized array measurements. A reverse-time data process is applied to the synchronized array measurements to obtain a plurality of dynamic particle parameters associated with subsurface locations. These dynamic particle parameters are stored in a form for display. Maximum values of the dynamic particle parameters may be interpreted as reservoir locations. The dynamic particle parameters may be particle displacement values, particle velocity values, particle acceleration values or particle pressure values. The sensors may be three-component sensors. Zero-phase frequency filtering of different ranges of interest may be applied. The data may be resampled to facilitate efficient data processing.

0054<figref idref="DRAWINGS">FIG. 9</figref> is illustrative of a computing system <b>300</b> and operating environment for implementing a general purpose computing device in the form of a computer <b>10</b>. Computer <b>10</b> includes a processing unit <b>11</b> that may include ‘onboard’ instructions <b>12</b>. Computer <b>10</b> has a system memory <b>20</b> attached to a system bus <b>40</b> that operatively couples various system components including system memory <b>20</b> to processing unit <b>11</b>. The system bus <b>40</b> may be any of several types of bus structures using any of a variety of bus architectures as are known in the art.

0055While one processing unit <b>11</b> is illustrated in <figref idref="DRAWINGS">FIG. 9</figref>, there may be a single central-processing unit (CPU) or a graphics processing unit (GPU), or both or a plurality of processing units. Computer <b>10</b> may be a standalone computer, a distributed computer, or any other type of computer.

0056System memory <b>20</b> includes read only memory (ROM) <b>21</b> with a basic input/output system (BIOS) <b>22</b> containing the basic routines that help to transfer information between elements within the computer <b>10</b>, such as during start-up. System memory <b>20</b> of computer <b>10</b> further includes random access memory (RAM) <b>23</b> that may include an operating system (OS) <b>24</b>, an application program <b>25</b> and data <b>26</b>.

0057Computer <b>10</b> may include a disk drive <b>30</b> to enable reading from and writing to an associated computer or machine readable medium <b>31</b>. Computer readable media <b>31</b> includes application programs <b>32</b> and program data <b>33</b>.

0058For example, computer readable medium <b>31</b> may include programs to process seismic data, which may be stored as program data <b>33</b>, according to the methods disclosed herein. The application program <b>32</b> associated with the computer readable medium <b>31</b> includes at least one application interface for receiving and/or processing program data <b>33</b>. The program data <b>33</b> may include seismic data acquired according to embodiments disclosed herein. At least one application interface may be associated with calculating a ratio of data components, which may be spectral components, for locating subsurface hydrocarbon reservoirs.

0059The disk drive may be a hard disk drive for a hard drive (e.g., magnetic disk) or a drive for a magnetic disk drive for reading from or writing to a removable magnetic media, or an optical disk drive for reading from or writing to a removable optical disk such as a CD ROM, DVD or other optical media.

0060Disk drive <b>30</b>, whether a hard disk drive, magnetic disk drive or optical disk drive is connected to the system bus <b>40</b> by a disk drive interface (not shown). The drive <b>30</b> and associated computer-readable media <b>31</b> enable nonvolatile storage and retrieval for application programs <b>32</b> and data <b>33</b> that include computer-readable instructions, data structures, program modules and other data for the computer <b>10</b>. Any type of computer-readable media that can store data accessible by a computer, including but not limited to cassettes, flash memory, digital video disks in all formats, random access memories (RAMs), read only memories (ROMs), may be used in a computer <b>10</b> operating environment.

0061Data input and output devices may be connected to the processing unit <b>11</b> through a serial interface <b>50</b> that is coupled to the system bus. Serial interface <b>50</b> may a universal serial bus (USB). A user may enter commands or data into computer <b>10</b> through input devices connected to serial interface <b>50</b> such as a keyboard <b>53</b> and pointing device (mouse) <b>52</b>. Other peripheral input/output devices <b>54</b> may include without limitation a microphone, joystick, game pad, satellite dish, scanner or fax, speakers, wireless transducer, etc. Other interfaces (not shown) that may be connected to bus <b>40</b> to enable input/output to computer <b>10</b> include a parallel port or a game port. Computers often include other peripheral input/output devices <b>54</b> that may be connected with serial interface <b>50</b> such as a machine readable media <b>55</b> (e.g., a memory stick), a printer <b>56</b> and a data sensor <b>57</b>. A seismic sensor or seismometer for practicing embodiments disclosed herein is a nonlimiting example of data sensor <b>57</b>. A video display <b>72</b> (e.g., a liquid crystal display (LCD), a flat panel, a solid state display, or a cathode ray tube (CRT)) or other type of output display device may also be connected to the system bus <b>40</b> via an interface, such as a video adapter <b>70</b>. A map display created from spectral ratio values as disclosed herein may be displayed with video display <b>72</b>.

0062A computer <b>10</b> may operate in a networked environment using logical connections to one or more remote computers. These logical connections are achieved by a communication device associated with computer <b>10</b>. A remote computer may be another computer, a server, a router, a network computer, a workstation, a client, a peer device or other common network node, and typically includes many or all of the elements described relative to computer <b>10</b>. The logical connections depicted in <figref idref="DRAWINGS">FIG. 9</figref> include a local-area network (LAN) or a wide-area network (WAN) <b>90</b>. However, the designation of such networking environments, whether LAN or WAN, is often arbitrary as the functionalities may be substantially similar. These networks are common in offices, enterprise-wide computer networks, intranets and the Internet.

0063When used in a networking environment, the computer <b>10</b> may be connected to a network <b>90</b> through a network interface or adapter <b>60</b>. Alternatively computer <b>10</b> may include a modem <b>51</b> or any other type of communications device for establishing communications over the network <b>90</b>, such as the Internet. Modem <b>51</b>, which may be internal or external, may be connected to the system bus <b>40</b> via the serial interface <b>50</b>.

0064In a networked deployment computer <b>10</b> may operate in the capacity of a server or a client user machine in server-client user network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. In a networked environment, program modules associated with computer <b>10</b>, or portions thereof, may be stored in a remote memory storage device. The network connections schematically illustrated are for example only and other communications devices for establishing a communications link between computers may be used.

0065While various embodiments have been shown and described, various modifications and substitutions may be made thereto without departing from the spirit and scope of the disclosure herein. Accordingly, it is to be understood that the present embodiments have been described by way of illustration and not limitation.